


'''simple docstring'''



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    @slow
    @require_torch
    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      List[Any]														=						EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,	'prajjwal1/bert-tiny'      )
         __lowerCamelCase  :      Tuple														=						BertTokenizer.from_pretrained('bert-base-uncased'      )

         __lowerCamelCase  :      Optional[int]														=						bertabert.config.encoder.vocab_size
         __lowerCamelCase  :      Any														=						tokenizer.sep_token_id
         __lowerCamelCase  :      List[Any]														=						tokenizer.cls_token_id
         __lowerCamelCase  :      List[str]														=						1_28

         __lowerCamelCase  :      Dict														=						datasets.load_dataset('cnn_dailymail'     ,	'3.0.0'     ,	split='train[:1%]'      )
         __lowerCamelCase  :      str														=						datasets.load_dataset('cnn_dailymail'     ,	'3.0.0'     ,	split='validation[:1%]'      )

         __lowerCamelCase  :      Dict														=						train_dataset.select(range(32      )      )
         __lowerCamelCase  :      str														=						val_dataset.select(range(16      )      )

         __lowerCamelCase  :      Any														=						4

         def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE_      ):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              __lowerCamelCase  :      int														=						tokenizer(batch['article']     ,	padding='max_length'     ,	truncation=SCREAMING_SNAKE_CASE_     ,	max_length=5_12      )
              __lowerCamelCase  :      List[Any]														=						tokenizer(batch['highlights']     ,	padding='max_length'     ,	truncation=SCREAMING_SNAKE_CASE_     ,	max_length=1_28      )
              __lowerCamelCase  :      str														=						inputs.input_ids
              __lowerCamelCase  :      List[str]														=						inputs.attention_mask

              __lowerCamelCase  :      Optional[Any]														=						outputs.input_ids
              __lowerCamelCase  :      Dict														=						outputs.input_ids.copy()
              __lowerCamelCase  :      Tuple														=						[
                  [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              __lowerCamelCase  :      Union[str, Any]														=						outputs.attention_mask

              assert all(len(SCREAMING_SNAKE_CASE_      ) == 5_12 for x in inputs.input_ids      )
              assert all(len(SCREAMING_SNAKE_CASE_      ) == 1_28 for x in outputs.input_ids      )

              return batch

         def _compute_metrics(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Tuple														=						pred.label_ids
              __lowerCamelCase  :      Optional[Any]														=						pred.predictions

              # all unnecessary tokens are removed
              __lowerCamelCase  :      List[Any]														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Tuple														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      List[Any]														=						sum([int(pred_str[i] == label_str[i]      ) for i in range(len(SCREAMING_SNAKE_CASE_      )      )]      ) / len(SCREAMING_SNAKE_CASE_      )

              return {"accuracy": accuracy}

         # map train dataset
         __lowerCamelCase  :      List[Any]														=						train_dataset.map(
             _map_to_encoder_decoder_inputs     ,	batched=SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	remove_columns=['article', 'highlights']     ,	)
         train_dataset.set_format(
             type='torch'     ,	columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,	)

         # same for validation dataset
         __lowerCamelCase  :      List[Any]														=						val_dataset.map(
             _map_to_encoder_decoder_inputs     ,	batched=SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	remove_columns=['article', 'highlights']     ,	)
         val_dataset.set_format(
             type='torch'     ,	columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,	)

         __lowerCamelCase  :      int														=						self.get_auto_remove_tmp_dir()

         __lowerCamelCase  :      Dict														=						SeqaSeqTrainingArguments(
             output_dir=SCREAMING_SNAKE_CASE_     ,	per_device_train_batch_size=SCREAMING_SNAKE_CASE_     ,	per_device_eval_batch_size=SCREAMING_SNAKE_CASE_     ,	predict_with_generate=SCREAMING_SNAKE_CASE_     ,	evaluation_strategy='steps'     ,	do_train=SCREAMING_SNAKE_CASE_     ,	do_eval=SCREAMING_SNAKE_CASE_     ,	warmup_steps=0     ,	eval_steps=2     ,	logging_steps=2     ,	)

         # instantiate trainer
         __lowerCamelCase  :      Any														=						SeqaSeqTrainer(
             model=SCREAMING_SNAKE_CASE_     ,	args=SCREAMING_SNAKE_CASE_     ,	compute_metrics=_compute_metrics     ,	train_dataset=SCREAMING_SNAKE_CASE_     ,	eval_dataset=SCREAMING_SNAKE_CASE_     ,	tokenizer=SCREAMING_SNAKE_CASE_     ,	)

         # start training
         trainer.train()








import math




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   0
							__UpperCamelCase      :  int       =   0
							while num > 0:
														__UpperCamelCase      :  List[Any]       =   num % 8
														__UpperCamelCase      :  Tuple       =   octal + (remainder * math.floor(math.pow(10		,    _lowerCamelCase)))
														counter += 1
														__UpperCamelCase      :  Optional[Any]       =   math.floor(num / 8)  # basically /= 8 without remainder if any
														# This formatting removes trailing '.0' from `octal`.
							return F'0o{int(_lowerCamelCase)}'




def       _SCREAMING_SNAKE_CASE   (						)   ->       None:






							'''simple docstring'''





							print("\n2 in octal is:")
							print(decimal_to_octal(2))  # = 2
							print("\n8 in octal is:")
							print(decimal_to_octal(8))  # = 10
							print("\n65 in octal is:")
							print(decimal_to_octal(65))  # = 101
							print("\n216 in octal is:")
							print(decimal_to_octal(216))  # = 330
							print("\n512 in octal is:")
							print(decimal_to_octal(512))  # = 1000
							print("\n")


if __name__ == "__main__":
		main()